Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems

Safe Distance and Face Mask Detection using OpenCV and MobileNetV2

Author(s): B.S. Maya*, T. Asha, P. Prajwal, P.N. Revanth, Pratik R Pailwan and Rahul Kumar Gupta

Pp: 76-95 (20)

DOI: 10.2174/9781681089553122010008

* (Excluding Mailing and Handling)


The COVID-19 epidemic affects humans irrespective of race, religion, standing, and caste. It has affected more than 20 million people worldwide. Wearing face masks and taking public safety measures are two advanced safety measures that need to be taken in open areas to prevent the spread of the disease. To create a secure environment that contributes to public safety, we propose a computer-based method that focuses on automatic real-time surveillance to identify safe general distance and face masks in public places using a model to monitor movement and detect camera violations. We achieve 97.6% specificity with the help of OpenCV and MobileNetV2 strategies.

Keywords: Coronavirus, Covid-19, Deep learning, Face-mask-detection, MobileNetV2, OpenCV, Safe-distancing, Transfer learning, YOLO-V3.

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